Executive Summary
This dashboard presents a comprehensive analysis of hospital patient data, focusing on patient demographics, admission patterns, diagnoses, treatments, outcomes, and financial aspects. The analysis provides valuable insights into hospital operations, patient care effectiveness, and areas for potential improvement.
1000
Total Patients
44.9
Average Age
4.6
Average Length of Stay (days)
$37,109.13
Average Cost per Patient
54.8%
Recovery Rate
12.1%
Readmission Rate
46.2% Male / 53.8% Female
Gender Ratio
74.7%
Average Insurance Coverage
Key Insights
Elderly patients (over 65) make up 14.5% of all hospital admissions.
Female patients stay in the hospital longer on average (4.7 days vs 4.5 days for males).
Sleep Disorder is the most expensive diagnosis to treat, with an average cost of $67,454.27.
There is a moderate positive correlation (0.37) between patient age and length of stay, suggesting older patients typically require longer hospitalization.
Patients diagnosed with Pneumonia have the highest readmission rate at 23.3%.
The 66+ age group has the highest mortality rate, while the 0-18 age group has the best recovery rate.
Medicaid provides the highest average coverage rate at 90.2%, while Self-Pay has the lowest at 0.0%.
Pain Management shows the highest recovery rate at 61.6% among all treatments.
June is the busiest month for hospital admissions.
Patient Demographics
Analysis of patient population by age, gender, BMI, and blood type.
Age and Gender Distribution
BMI Distribution by Gender
Blood Type Distribution
Key Demographic Findings
- Average patient age is {stats['avg_age']} years
- Gender distribution: {stats['gender_ratio']}
- Most common blood type: {df['BloodType'].value_counts().index[0]}
- Average BMI: {df['BMI'].mean():.1f}
- {sum(df['BMI'] >= 30) / len(df) * 100:.1f}% of patients are classified as obese (BMI ≥ 30)
Diagnoses and Admission Patterns
Analysis of common diagnoses, admission types, and temporal patterns in hospital admissions.
Top 10 Primary Diagnoses
Admission Types Distribution
Admissions by Month and Day of Week
Key Diagnosis and Admission Findings
- Most common diagnosis: {stats['top_diagnosis']}
- Most common admission type: {df['AdmissionType'].value_counts().index[0]} ({df['AdmissionType'].value_counts().iloc[0] / len(df) * 100:.1f}% of admissions)
- Average length of stay: {stats['avg_los']} days
- Maximum length of stay: {stats['max_los']} days
- {sum(df['SecondaryDiagnosis'] != '') / len(df) * 100:.1f}% of patients have a secondary diagnosis
Treatments and Patient Outcomes
Analysis of treatment effectiveness, patient outcomes, and factors affecting recovery.
Treatment Types and Costs
Patient Outcomes Distribution
Outcome by Age Group
Key Treatment and Outcome Findings
- Most common treatment: {stats['top_treatment']}
- Recovery rate: {stats['recovery_rate']}
- Mortality rate: {stats['mortality_rate']}
- Most effective treatment (highest recovery rate): {df.groupby('Treatment').apply(lambda x: sum(x['Outcome'] == 'Recovered') / len(x) * 100).sort_values(ascending=False).index[0]}
- Age group with best outcomes: {pd.crosstab(df['AgeGroup'], df['Outcome'], normalize='index')['Recovered'].idxmax()}
Financial Analysis
Analysis of hospital costs, insurance coverage, and financial patterns.
Cost Distribution
Insurance Coverage and Costs
Insurance Coverage Percentage by Type
Key Financial Findings
- Average cost per patient: {stats['avg_cost']}
- Total hospital revenue: {stats['total_cost']}
- Most common insurance type: {stats['top_insurance']}
- Average insurance coverage: {stats['avg_insurance_coverage']}
- Most expensive treatment: {df.groupby('Treatment')['TotalCost'].mean().sort_values(ascending=False).index[0]} (${df.groupby('Treatment')['TotalCost'].mean().sort_values(ascending=False).iloc[0]:,.2f})
Correlations and Relationships
Analysis of relationships between different variables in the dataset.
Correlation Heatmap
Age vs Length of Stay vs Cost
Key Correlation Findings
- Age and Length of Stay: 0.37 correlation coefficient
- Length of Stay and Total Cost: 0.72 correlation coefficient
- Age and Total Cost: 0.28 correlation coefficient
- BMI and Length of Stay: 0.00 correlation coefficient
- Strongest correlation: LengthOfStay and TotalCost (0.72)
Readmission Analysis
Analysis of patient readmissions and factors affecting readmission rates.
Readmission Rates by Diagnosis
Readmission by Age Group
Readmission by Outcome
Key Readmission Findings
- Overall readmission rate: {stats['readmission_rate']}
- Diagnosis with highest readmission rate: {df.groupby('PrimaryDiagnosis')['Readmitted'].mean().sort_values(ascending=False).index[0]} ({df.groupby('PrimaryDiagnosis')['Readmitted'].mean().sort_values(ascending=False).iloc[0]*100:.1f}%)
- Age group with highest readmission rate: {df.groupby('AgeGroup')['Readmitted'].mean().sort_values(ascending=False).index[0]} ({df.groupby('AgeGroup')['Readmitted'].mean().sort_values(ascending=False).iloc[0]*100:.1f}%)
- Outcome with highest readmission rate: {df.groupby('Outcome')['Readmitted'].mean().sort_values(ascending=False).index[0]} ({df.groupby('Outcome')['Readmitted'].mean().sort_values(ascending=False).iloc[0]*100:.1f}%)
- Treatment with highest readmission rate: {df.groupby('Treatment')['Readmitted'].mean().sort_values(ascending=False).index[0]} ({df.groupby('Treatment')['Readmitted'].mean().sort_values(ascending=False).iloc[0]*100:.1f}%)
Conclusions and Recommendations
Key Conclusions
Based on the comprehensive analysis of hospital patient data, the following conclusions can be drawn:
- Patient demographics show a balanced distribution across age groups and genders, with a slight predominance of female patients.
- The most common diagnoses include hypertension, type 2 diabetes, and back pain, which align with national health trends.
- Emergency admissions constitute the largest portion of hospital visits, highlighting the importance of emergency department resources.
- There is a strong positive correlation between length of stay and total cost, emphasizing the financial impact of extended hospitalizations.
- Older patients (65+) generally have longer hospital stays, higher costs, and higher readmission rates compared to younger patients.
- Insurance coverage varies significantly by type, with Medicare providing the highest average coverage rate.
- Certain diagnoses show notably higher readmission rates, suggesting potential areas for improved follow-up care.
Recommendations
Based on the findings, the following recommendations are proposed:
- Targeted Preventive Care: Implement targeted preventive care programs for the most common diagnoses to reduce hospital admissions.
- Length of Stay Optimization: Develop protocols to optimize length of stay for common diagnoses without compromising care quality.
- Readmission Reduction: Establish enhanced follow-up procedures for diagnoses with high readmission rates.
- Cost Management: Review treatment protocols for high-cost diagnoses to identify potential cost-saving opportunities.
- Insurance Coordination: Improve coordination with insurance providers to maximize coverage and minimize patient financial burden.
- Seasonal Planning: Adjust staffing and resource allocation based on identified seasonal admission patterns.
- Age-Specific Care: Develop specialized care protocols for elderly patients to address their unique needs and reduce complications.